Receiver Functions from Autoregressive Deconvolution
Receiver Functions from Autoregressive Deconvolution
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DOI：
10.1007/s00024-007-0269-5
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发表时间：
2007-12
影响因子：
2
通讯作者：
Qingju Wu;Yonghua Li;Ruiqing Zhang;Rong-sheng Zeng
中科院分区：
文献类型：
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作者：
Qingju Wu;Yonghua Li;Ruiqing Zhang;Rong-sheng Zeng
接收函数可以通过最小化维纳滤波的时域平方误差或频域频谱分割的平方误差来估计。为了避免直接计算Toeplitz方程中的自相关系数和互相关系数，或直接计算分频方程中的自谱和互谱，以及经验地选择阻尼参数，提出了自回归反褶积的方法，将接收函数从三分量远震P波中分离出来。远震P波的垂直分量用自回归模型来模拟，它可以分别向前和向后预测。由Akaike准则确定自回归模型的最优长度。通过最小化前向和后向预测滤波器的平方误差，可以递推求解自回归滤波器系数，并以类似的方法估计接收函数。模拟和实际资料检验都表明，自回归反褶积是一种在时间域内从远震P波中分离接收函数的有效方法。
Receiver functions can be estimated by minimizing the square errors of Wiener filter in time-domain or spectrum division in frequency domain. To avoid the direct calculation of auto-correlation and cross-correlation coefficients in Toeplitz equation or of auto-spectrum and cross-spectrum in spectrum division equation as well as empirically choosing a damping parameter, autoregressive deconvolution is presented to isolate receiver function from three-component teleseismic P waveforms. The vertical component of teleseismic P waveform is modeled by an autoregressive model, which can be forward and backward, predicted respectively. The optimum length of the autoregressive model is determined by the Akaike criterion. By minimizing the square errors of forward and backward predicting filters, autoregressive filter coefficients can be recursively solved, and receiver function is also estimated in the similar procedure. Both synthetic and real data tests show that autoregressive deconvolution is an effective method to isolate receiver function from teleseismic P waveforms in time-domain.